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Diffstat (limited to 'model-integration/src/test/models/tensorflow/softmax/softmax.py')
| -rw-r--r-- | model-integration/src/test/models/tensorflow/softmax/softmax.py | 29 |
1 files changed, 0 insertions, 29 deletions
diff --git a/model-integration/src/test/models/tensorflow/softmax/softmax.py b/model-integration/src/test/models/tensorflow/softmax/softmax.py deleted file mode 100644 index aab9956f914..00000000000 --- a/model-integration/src/test/models/tensorflow/softmax/softmax.py +++ /dev/null @@ -1,29 +0,0 @@ -# Copyright 2019 Oath Inc. Licensed under the terms of the Apache 2.0 license. See LICENSE in the project root. - -import numpy as np -import tensorflow as tf - -# Creates simple random neural network that has softmax on output. No training. - -n_inputs = 5 -n_outputs = 3 - -input = tf.placeholder(tf.float32, shape=(None, n_inputs), name="input") -W = tf.Variable(tf.random.uniform([n_inputs, n_outputs]), name="weights") -b = tf.Variable(tf.random.uniform([n_outputs]), name="bias") -Z = tf.matmul(input, W) + b -hidden_layer = tf.nn.relu(Z) -output_layer = tf.nn.softmax(hidden_layer, name="output") - -init = tf.global_variables_initializer() - -with tf.Session() as sess: - init.run() - export_path = "saved" - builder = tf.saved_model.builder.SavedModelBuilder(export_path) - signature = tf.saved_model.signature_def_utils.predict_signature_def(inputs = {'x':input}, outputs = {'y':output_layer}) - builder.add_meta_graph_and_variables(sess, - [tf.saved_model.tag_constants.SERVING], - signature_def_map={'serving_default':signature}) - builder.save(as_text=True) - |